This project focuses on automating the credit card application assessment process using advanced machine learning techniques, including Random Forest, Gradient Boosting, SVMs, Logistic Regression, Regularization Methods, and Hyperparameter Tuning. The objective is to improve the efficiency, accuracy, and fairness of credit card approval decisions. Historical credit card application data, comprising applicant demographics, financial history, and employment details, is collected and pre-processed. Feature engineering and exploratory data analysis (EDA) enhance the dataset’s predictive power. Three machine learning algorithms, Random Forest, Logistic Regression, and Gradient Boosting are applied. Regularization techniques (L1 and L2) and hyperparameter tuning are used to prevent overfitting and optimize model performance. The project assesses model performance by employing metrics such as accuracy, precision, recall, F1-score, and ROC-AUC metrics, and conducts feature importance analysis to identify key factors influencing approval decisions. The project aims to deliver robust, accurate, and fair credit card approval models, benefitting both financial institutions and applicants

Diabetic foot ulcers (DFU) and infections are the most common complications of diabetic foot disease. Mortality and financial burdens for both patients and society on the whole are caused by the prevalence of complications. Peripheral neuropathy, peripheral arterial disease, and immune response dysfunction are just a few of the main contributing factors that must be understood in order to effectively treat the condition. In order to treat diabetic foot disease, you must first get a comprehensive physical examination and a detailed history of your condition. Diabetic neuropathy and peripheral vascular disease, as well as any evidence of diabetic foot ulcers or infection, should be examined during this procedure. Patients with diabetes mellitus were studied to see if there was a link between cognitive impairment and the condition of their feet and whether or not they followed their doctor's recommendations for glycemic control. Using a random sample of diabetes patients, researchers conducted a prospective study to see how many people had the condition. The Mini-Mental State Valuation, the Trail Making Judgments, and the Michigan Screening Instrument were used to assess cognitive abilities. In the one-month follow-up, glycated hemoglobin (HB1Ac >7 %) was linked to the MMSE and medication adherence, but no link was seen between cognitive function and neuropathy. According to the results of a ROC curve investigation, HB1Ac and the MNSI score both significantly (p< 0,05) mitigate towards eventual adherence to medicine for foot problems. For the purpose of determining if DFU was associated with cognitive impairment, the Chi square valuation was used in the statistical examination. As a determinant of MMSE and MoCA scores, the researchers used linear regression to come to their conclusion. Diabetic foot issues should be managed with good blood sugar control and less acute neuropathy, and this does not seem to be linked to cognitive dysfunction. More study is required in order to personalize treatments for diseases of the central and peripheral nervous systems appropriately. Cognitive dysfunction should be taken into account by doctors and podiatrists while treating diabetic foot problems